Software development tools can be used for generating, testing, and deploying program code that implements the functionality of analytical or predictive models. For example, an analytical model can be used for identifying relationships between sets of predictor variables (e.g., datasets representing actions performed by entities or transactions between entities) and one or more output variables (e.g., the probability of an entity performing some task or meeting some criteria).
A software development tool can be used to develop a model using data extracted from a data archive or other data source. Data mining and statistical methods can be used to develop model specifications. The software development tool can generate program code that is executed by data-processing platforms models in production environments. A production environment can include one or more computing systems for testing and implementing various operations that are represented by a model (e.g., risk prediction). When the program code is executed in the production environment, the efficacy of a model can be evaluated, and the program code for implementing the model can be updated based on the evaluation.
Relying on the evaluation of the deployed programming code can present disadvantages. For example, the deployment and execution of program code for implementing a model can utilize computing resources (e.g., network bandwidth, processing cycles, memory, etc.) that may be otherwise deployed in a data-processing environment. If an executing of program code for implementing a model results in errors, a debugging process must distinguish errors specific to the software platform (e.g., coding errors for C++, Java, etc.) from errors in the modeling logic (e.g., improper training or calibration of the model). Thus, in some cases, relying on platform-specific evaluations of an analytical model may inefficiently utilize available computing resources.